Cost Minimization of Cloud Services for On-Demand Video Streaming

TL;DR

This paper presents an optimization method for cloud storage of videos in on-demand streaming, significantly reducing costs by intelligently selecting storage locations based on access patterns.

Contribution

It introduces a novel approach to minimize cloud storage costs for video streaming by optimizing storage placement based on usage data.

Findings

Cost reduction of up to 22% using the proposed method.

Effectiveness increases with higher video access frequency.

Applicable to large-scale video repositories.

Abstract

Cloud Technology is adopted to process video streams because of the great features provided to video stream providers such as the high flexibility of using virtual machines and storage servers at low rates. Video stream providers prepare several formats of the same video to satisfy all users' devices' specifications. Video streams in the cloud are either transcoded or stored. However, storing all formats of videos is still costly. In this research, we develop an approach that optimizes cloud storage. Particularly, we propose a method that decides which video in which cloud storage should be stored to minimize the overall cost of cloud services. The results of the proposed approach are promising, it shows effectiveness when the number of frequently accessed video grow in a repository, and when the views of videos increases. The proposed method decreases the cost of using cloud services by up to 22%.